Video Super-Resolution Using a Grouped Residual in Residual Network
This is an incremental improvement for video processing applications, addressing quality enhancement in super-resolution.
The authors tackled video super-resolution by proposing a grouped residual in residual network (GRRN), which achieved acceptable output image quality but did not outperform existing methods on some quantitative metrics.
Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement. Video super-resolution (VSR) can be considered as the generalization of single image super-resolution (SISR). This generalization should be such that more detail is created in the output using adjacent input frames. In this paper, we propose a grouped residual in residual network (GRRN) for VSR. By adjusting the hyperparameters of the proposed structure, we train three networks with different numbers of parameters and compare their quantitative and qualitative results with the existing methods. Although based on some quantitative criteria, GRRN does not provide better results than the existing methods, in terms of the quality of the output image it has acceptable performance.